Trace Norm Generative Adversarial Networks for Sensor Generation and Feature Extraction

Generative Adversarial Networks (GANs) have been shown effective to generate realistic enough sensor data for industrial failure prediction. Compared to computer vision problems, where it is very common to have more than 1000 classes, the number of classes for industrial problems is very small. For instance, only two classes, failure or non-failure, are considered in failure prediction. When using GANs to generate industrial sensor data, many of the generated samples are from the same class and thus are highly correlated. However, this relationships among generated samples are not explicitly considered in existing GAN frameworks. This type of correlations can be captured by trace norm minimization. In this work, we propose Trace Norm GANs to enforce trace norm minimization on generated samples. Extensive experiments on real industrial data show that (1) Trace Norm GANs improve generated samples in terms of quality/diversity metrics, (2) Trace Norm GANs extract efficient features for supervised learning tasks.